Remote Data Labeling Jobs

Remote data labeling jobs on Rex.zone connect skilled annotators with real AI/ML training workflows. These roles span RLHF (Reinforcement Learning from Human Feedback), LLM prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality and model performance. As a core job entity, a data labeling specialist applies annotation guidelines, ensures annotation guidelines compliance, and supports large language model evaluation within LLM training pipelines. Apply to join projects from AI labs, tech startups, BPOs, and annotation vendors across NLP, vision, and safety. Work remote as freelance, contract, part-time, or full-time—entry-level to senior.

Key Responsibilities

Provide high-fidelity annotations for text, image, video, and audio; execute named entity recognition, document classification, bounding boxes, polygons, segmentation, and redaction; rate and compare model outputs for RLHF and prompt evaluation; conduct content safety labeling (toxicity, violence, self-harm, NSFW) using calibrated rubrics; achieve training data quality targets with gold tasks and inter-annotator agreement; follow annotation guidelines compliance; perform QA evaluation and error analysis; contribute to taxonomy refinement; document edge cases; support large language model evaluation and model performance improvement cycles in production-grade LLM training pipelines.

Required Qualifications

Proven attention to detail and consistency; fluency in English (additional languages a plus); familiarity with NLP tasks (NER, sentiment, summarization) and/or computer vision annotation; ability to follow SOPs, taxonomies, and quality rubrics; basic understanding of RLHF, LLM behaviors, and safety policies; comfort with web-based labeling tools and productivity trackers; reliable remote setup with secure internet; availability to meet throughput and quality SLAs; openness to feedback, calibration sessions, and iterative guideline updates.

Nice to Have

Experience at AI labs, tech startups, BPOs, or annotation vendors; prior work in content moderation or safety policy enforcement; scripting with Python, regex, or SQL for data checks; familiarity with prompt engineering and error taxonomies; background in linguistics or domain-specific labeling (legal, medical, finance); multilingual capability; understanding of accessibility, bias mitigation, and fairness; comfort with privacy, security, and compliance best practices.

Work Types and Schedules

Opportunities include remote freelance, contract, part-time, and full-time roles, plus temporary surges and internships. We staff entry-level and senior specialist tracks, including reviewer, lead annotator, and QA roles. Projects run across time zones with options for flexible hours or structured shifts. Compensation structures vary by hourly, per-task, or milestone with performance incentives tied to accuracy, speed, and audit pass rates.

Domains We Hire For

NLP (NER, summarization, sentiment, classification, RAG evaluation), computer vision (detection, segmentation, OCR), content safety labeling (policy taxonomies and risk tiers), LLM training and evaluation (RLHF preferences, prompt evaluation, safety red-teaming), speech/audio (ASR, diarization, intent). Employers include AI labs, tech startups, BPOs, and specialized annotation vendors building high-quality datasets.

Quality and Tooling

Work with modern labeling platforms and review queues; use calibrated rubrics, golden sets, spot checks, consensus workflows, and inter-annotator agreement. Apply clear annotation guidelines, edge-case handling, and escalation paths. Support versioned taxonomies, data lineage, and privacy controls. Participate in calibration, feedback cycles, and periodic retraining to sustain training data quality and model performance improvement.

Application Process

Apply on Rex.zone to create a candidate profile. Complete a skills screening and a short annotation challenge aligned to project guidelines. Successful applicants may join project rosters, complete onboarding, and begin paid work after meeting baseline accuracy thresholds. Advancement to reviewer or QA roles depends on sustained quality metrics and leadership aptitude.

Compensation and Benefits

Competitive pay by hour or task with performance bonuses; recurring project opportunities; remote-friendly policies; access to diverse AI/ML datasets and evaluations; pathway to senior annotator, reviewer, or QA lead roles. Exact rates vary by domain complexity, language, and employer type.

Remote Data Labeling Jobs: FAQ

Q: What does a remote data labeling specialist do?
They create high-quality annotations for text, image, video, and audio, conduct RLHF and prompt evaluations, follow guidelines, and meet QA and throughput goals to improve AI/ML model performance.
Q: Who hires for these roles on Rex.zone?
AI labs, tech startups, BPOs, and specialized annotation vendors hire for NLP, computer vision, content safety labeling, and LLM training and evaluation projects.
Q: Is prior experience required?
Entry-level roles are available with training and calibration. Prior annotation, moderation, or NLP/CV experience is preferred for senior, reviewer, or QA positions.
Q: What employment types are available?
Full-time, part-time, contract, freelance, and temporary roles. Compensation may be per-task or hourly, with bonuses tied to accuracy and audit pass rates.
Q: How do RLHF and prompt evaluation fit in?
Annotators compare and rate model outputs, provide preference signals, and assess safety to guide large language model evaluation and continuous model improvement.
Q: What tools and quality processes are used?
Web-based labeling tools with golden sets, spot checks, consensus, and inter-annotator agreement. Calibration and feedback cycles maintain training data quality.
Q: How do I apply?
Create a profile on Rex.zone, complete a skills screening and a short annotation challenge, and join project rosters after meeting the accuracy threshold.

230+Domains Covered

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50+Countries Represented

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